





"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :list[list[int]]											=				[[0 for _ in range(_SCREAMING_SNAKE_CASE     )] for _ in range(m + 1     )]
  for i in range(m + 1     ):
    lowerCAmelCase__     :Union[str, Any]											=				1

  for n in range(m + 1     ):
    for k in range(1	,  _SCREAMING_SNAKE_CASE     ):
      memo[n][k] += memo[n][k - 1]
      if n - k > 0:
        memo[n][k] += memo[n - k - 1][k]

  return memo[m][m - 1]


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    try:
      __A											=						int(input("""Enter a number: """).strip())
      print(partition(n))
    except ValueError:
      print("""Please enter a number.""")
  else:
    try:
      __A											=						int(sys.argv[1])
      print(partition(n))
    except ValueError:
      print("""Please pass a number.""")

import datasets


__a    :	Optional[Any]         =    """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__a    :	Optional[Any]         =    """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__a    :	str         =    """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				return (preds == labels).mean()




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							        }		) ,  codebase_urls=[] ,  reference_urls=[] ,  format='''numpy''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							return {"accuracy": simple_accuracy(lowerCAmelCase__ ,  lowerCAmelCase__		)}